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Combination of a novel gene expression signature with a clinical nomogram improves the prediction of survival in high-risk bladder cancer.

ABSTRACT: We aimed to validate and improve prognostic signatures for high-risk urothelial carcinoma of the bladder.We evaluated microarray data from 93 patients with bladder cancer managed by radical cystectomy to determine gene expression patterns associated with clinical and prognostic variables. We compared our results with published bladder cancer microarray data sets comprising 578 additional patients and with 49 published gene signatures from multiple cancer types. Hierarchical clustering was utilized to identify subtypes associated with differences in survival. We then investigated whether the addition of survival-associated gene expression information to a validated postcystectomy nomogram utilizing clinical and pathologic variables improves prediction of recurrence.Multiple markers for muscle invasive disease with highly significant expression differences in multiple data sets were identified, such as fibronectin 1 (FN1), NNMT, POSTN, and SMAD6. We identified signatures associated with pathologic stage and the likelihood of developing metastasis and death from bladder cancer, as well as with two distinct clustering subtypes of bladder cancer. Our novel signature correlated with overall survival in multiple independent data sets, significantly improving the prediction concordance of standard staging in all data sets [mean ?C-statistic: 0.14; 95% confidence interval (CI), 0.01-0.27; P < 0.001]. Tested in our patient cohort, it significantly enhanced the performance of a postoperative survival nomogram (?C-statistic: 0.08, 95% CI, -0.04-0.20; P < 0.005).Prognostic information obtained from gene expression data can aid in posttreatment prediction of bladder cancer recurrence. Our findings require further validation in external cohorts and prospectively in a clinical trial setting.

SUBMITTER: Riester M

PROVIDER: S-EPMC3569085 | biostudies-literature | 2012 Mar

REPOSITORIES: biostudies-literature

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Combination of a novel gene expression signature with a clinical nomogram improves the prediction of survival in high-risk bladder cancer.

Riester Markus M Taylor Jennifer M JM Feifer Andrew A Koppie Theresa T Rosenberg Jonathan E JE Downey Robert J RJ Bochner Bernard H BH Michor Franziska F

Clinical cancer research : an official journal of the American Association for Cancer Research 20120106 5

<h4>Purpose</h4>We aimed to validate and improve prognostic signatures for high-risk urothelial carcinoma of the bladder.<h4>Experimental design</h4>We evaluated microarray data from 93 patients with bladder cancer managed by radical cystectomy to determine gene expression patterns associated with clinical and prognostic variables. We compared our results with published bladder cancer microarray data sets comprising 578 additional patients and with 49 published gene signatures from multiple canc ...[more]

PMID: 22228636

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Project description:Traditional histopathology performed by pathologists through naked eyes is insufficient for accurate survival prediction of bladder cancer (BCa). In addition, how neutrophil to lymphocyte ratio (NLR) could be used for prognosis prediction of BCa patients has not been fully understood. In this study, we collected 508 whole slide images (WSIs) of hematoxylin-eosin strained BCa slices and NLR value from the Shanghai General Hospital and The Cancer Genome Atlas (TCGA), which were further processed for nuclear segmentation. Cross-verified prediction models for predicting clinical prognosis were constructed based on machine learning methods. Six WSIs features were selected for the construction of pathomics-based prognosis model, which could automatically distinguish BCa patients with worse survival outcomes, with hazard ratio value of 2.19 in TCGA cohort (95% confidence interval: 1.63-2.94, p <0.0001) and 3.20 in General cohort (95% confidence interval: 1.75-5.87, p = 0.0014). Patients in TCGA cohort with high NLR exhibited significantly worse clinical survival outcome when compared with patients with low NLR (HR = 2.06, 95% CI: 1.29-3.27, p <0.0001). External validation in General cohort also revealed significantly poor prognosis in BCa patients with high NLR (HR = 3.69, 95% CI: 1.83-7.44 p <0.0001). Univariate and multivariate cox regression analysis proved that both the MLPS and the NLR could act as independent prognostic factor for overall survival of BCa patients. Finally, a novel nomogram based on MLPS and NLR was constructed to improve their clinical practicability, which had excellent agreement with actual observation in 1-, 3- and 5-year overall survival prediction. Decision curve analyses both in the TCGA cohort and General cohort revealed that the novel nomogram acted better than both the tumor grade system in prognosis prediction. Our novel nomogram based on MLPS and NLR could act as an excellent survival predictor and provide a scalable and cost-effective method for clinicians to facilitate individualized therapy. Nevertheless, prospective studies are still needed for further verifications.

Project description:Background: Head and neck squamous cell carcinoma (HNSCC) is a highly prevalent and malignant tumor that is difficult to effectively prognosticate outcomes. Recent reports have suggested that inflammation is strongly related to tumor progression, and several biomarkers linked to inflammation have been demonstrated to be useful for making a prognosis. The goal of this research was to explore the relevance between the inflammatory-related genes and HNSCC prognosis. Methods: The clinical information and gene expression data of patients with HNSCC were acquired from publicly available data sources. A multigene prognostic signature model was constructed in The Cancer Genome Atlas and verified in the Gene Expression Omnibus database. According to the risk score calculated for each patient, they were divided into low- and high-risk groups based on the median. The Kaplan-Meier survival curve and receiver operating characteristic curve were applied to determine the prognostic value of the risk model. Further analysis identified the independent prognostic factors, and a prognostic nomogram was built. The relationship between tumor immune infiltration status and risk scores was investigated using Spearman correlation analysis. Finally, to confirm the expression of genes in HNSCC, quantitative real-time polymerase chain reaction (qRT-PCR) was performed. Results: A prognostic model consisting of 14 inflammatory-related genes was constructed. The samples with a high risk had an apparently shorter overall survival than those with a low risk. Independent prognostic analysis found that risk scores were a separate prognostic factor in HNSCC patients. Immune infiltration analysis suggested that the abundance of B cells, CD8 T cells, M2 macrophages, myeloid dendritic cells, and monocytes in the low-risk group was higher, while that of M0, M1 macrophages, and resting NK cells was obviously higher in the high-risk group. The risk scores were related to chemotherapeutic sensitivity and the expression of several immune checkpoint genes. Moreover, CCL22 and IL10 were significantly higher in HNSCC tissues, as determined by qRT-PCR. Conclusion: Taken together, we constructed a novel inflammatory response-related gene signature, which may be used to estimate outcomes for patients with HNSCC and may be developed into a powerful tool for forecasting the efficacy of immunotherapeutic and chemotherapeutic drugs for HNSCC.

Project description:Objective Numerous microRNAs (miRNAs) have been identified in ccRCC and recommended to be used for predicting clear cell renal cell carcinoma (ccRCC) prognosis. However, it is not clear whether a miRNA-based nomogram results in improved survival prediction in patients with ccRCC. Methods miRNA profiles from tumors and normal tissues were downloaded from The Cancer Genome Atlas (TCGA) database and analyzed using the “limma” package. The association between differentially expressed miRNAs and patient prognosis was identified using univariate, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analyses. Next, all patients were randomly divided into development and validation cohorts at a ratio of 1?:?1. A nomogram was established based on independent prognostic factors in the development cohort. The prognostic performance of the nomogram was validated in both cohorts using the concordance index (C-index) and calibration plots. Results Multivariate Cox analysis identified the 13-miRNA signature, as well as AJCC stage and age, as independent prognostic factors after adjusting for other clinical covariates. The nomogram was built based on the independent variables. In the development cohort, the C-index for the constructed nomogram to predict overall survival (OS) was 0.792, which was higher than the C-index (0.731) of the AJCC staging system and C-index (0.778) of the miRNA signature. The nomogram demonstrated good discriminative ability in the validation cohort in predicting OS, with a C-index of 0.762. The calibration plots indicated an excellent agreement between the nomogram predicted survival probability and the actual observed outcomes. Furthermore, decision curve analysis (DCA) indicated that the nomogram was superior to the AJCC staging system in increasing the net clinical benefit. Conclusions The novel proposed nomogram based on a miRNA signature is a more reliable and robust tool for predicting the OS of patients with ccRCC compared to AJCC staging system, thus, improving clinical decision-making.

Dataset Information

Combination of a novel gene expression signature with a clinical nomogram improves the prediction of survival in high-risk bladder cancer.

Publications

Combination of a novel gene expression signature with a clinical nomogram improves the prediction of survival in high-risk bladder cancer.

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